9 Tools for Finding App Ideas from Real Reviews (2026, Compared)

There is a category confusion at the centre of this topic, and it costs people weeks. “App research tools” covers two completely different jobs: measuring how a market performs (downloads, revenue, keyword rank) and reading what users in it actually say. Most of the well-known tools do the first. Almost none do the second across competing apps.

This comparison is limited to that second question, what you can use to find a real, recurring, unfixed complaint. It is honest about which tools are not built for it.

The nine, compared

Prices move; treat them as a signpost and check the vendor. The two columns in the middle are the ones that matter, and they split the field almost perfectly in half.

The split nobody mentions: your apps vs the market’s

AppFollow, Appbot and Appfigures are genuinely good at reading review text. The catch is structural rather than a product weakness. The App Store only exposes full review data for apps you own. So those tools answer “what do my users complain about” extremely well, and cannot answer “do my competitors have this problem too.”

It is also why “just export your reviews and ask an AI”, which is good advice and you should do it, cannot produce a market finding. The export is scoped to you by design.

The other half don’t read reviews at all

AppKittie, Sensor Tower, AppTweak and MobileAction are frequently recommended for finding app ideas. They measure estimated downloads, estimated revenue, keyword rankings, ad creatives and category trends. All useful for sizing a market. None of it involves reading a review.

IdeaBrowser is a different shape again: curated ideas delivered on a cadence, with a large audience behind it. It answers “give me something to build,” not “prove this market is broken.”

BigIdeasDB is the closest comparison to what this site does, and the difference is method. It aggregates seven sources (Reddit, G2, Capterra, Upwork, app stores) and extracts findings with an LLM. Breadth of source, summarised. The trade is that an LLM asked “what are the top complaints” produces narrative-shaped answers whose arithmetic you cannot check.

Why counting and summarising are not the same thing

This is the part worth being precise about, because it is the whole difference between a finding you can act on and a sentence that sounds true.

Ask a language model to summarise 3,000 reviews and it will give you a confident, readable, plausible list of themes. What it will not give you is a number you can verify, because it never counted anything, it pattern-matched a summary. Ask “how many apps have this problem” and you get a figure that feels specific and has no arithmetic behind it.

The alternative is to classify each review individually, one review and one label, and then do ordinary arithmetic over the labels. How many reviews carry theme X. How many distinct apps carry theme X. What that theme’s rate is here versus its rate across every other category you have classified. Every one of those is a COUNT, which means the underlying reviews can be shown.

That comparison is the load-bearing part, and it is why the baseline matters more than the sample. “15% of complaints are about sync” means nothing on its own. It means something once you know the same theme runs at 2.2% across 11,434 other apps.

What GummySearch’s closure says about this category

GummySearch shut down on 30 November 2025. It was the best-known tool in adjacent territory, mining Reddit for pain points rather than app stores, and by search visibility it was the largest player in the space by a wide margin.

It is worth understanding why, because it generalises. GummySearch was built entirely on Reddit’s API, and Reddit repriced that API. A research tool whose corpus lives behind somebody else’s API does not control its own cost base, and when the terms change there is no product left.

This applies to every tool on the list, including this one, and it is worth asking a vendor about: what happens to your product if the source changes its terms? A tool that queries live and stores nothing dies the same week. A tool with a banked corpus loses freshness and keeps working.

How to choose

• You have a live app and want to fix it: AppFollow, Appbot or Appfigures. Reading your own reviews properly is the highest-value thing you can do, and these do it well.
• You want to know whether a market is broken before you build — you need cross-app counts, which is App Imp’s entire remit.
• You want volume of ideas across many surfaces: BigIdeasDB for the source breadth, IdeaBrowser if you want them curated and delivered.
• You want to size a market: AppKittie, Sensor Tower or Appfigures. Reviews are the wrong tool for that question.
• You want full control and have engineering time: Apify or your own scraper. Budget for the classification step, which is the hard part.

Disclosure

App Imp is ours. It counts complaints across every app in a category and ranks what nobody has fixed, from 2,029,091 US App Store reviews across 13,766 apps. Every category ranking and every deep report is free to read with no signup; the €9 charge generates a category nobody has analysed yet, and the result then becomes public like the rest.

It is a bad fit if you want Reddit data, revenue estimates, Play Store coverage or a curated idea feed, three of the tools above do those things better, which is why they are on the list.

Where to start

The ranked ideas board ranks categories by counted gaps, and the category index has the free complaint rankings. Neither asks for an email.